AIMC Topic: Humans

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BN-SNN: Spiking neural networks with bistable neurons for object detection.

PloS one
Spiking neural networks (SNNs) are emerging as a promising evolution in neural network paradigms, offering an alternative to conventional convolutional neural networks (CNNs). One of the most effective methods for SNN development is the CNN-to-SNN co...

Multi-scale time series prediction model based on deep learning and its application.

PloS one
Time series prediction is a widely used key technology, and traffic flow prediction is its typical application scenario. Traditional time series prediction models such as LSTM (Long Short- Term Memory) and CNN (Convolution Neural Network)-based model...

Nuclei segmentation and classification from histopathology images using federated learning for end-edge platform.

PloS one
Accurate nuclei segmentation and classification in histology images are critical for cancer detection but remain challenging due to color inconsistency, blurry boundaries, and overlapping nuclei. Manual segmentation is time-consuming and labor-intens...

Trajectory tracking and obstacle avoidance in dynamic environments using an improved artificial potential field method.

PloS one
Ensuring that a robot employing demonstration learning models can simultaneously achieve accurate trajectory tracking of demonstrated paths and effective avoidance of moving obstacles in dynamic environments remains a critical research challenge. Thi...

Mitosis detection in histopathological images using customized deep learning and hybrid optimization algorithms.

PloS one
Identifying mitosis is crucial for cancer diagnosis, but accurate detection remains difficult because of class imbalance and complex morphological variations in histopathological images. To overcome this challenge, we propose a Customized Deep Learni...

Multi-objective production scheduling optimization strategy based on fuzzy mathematics theory.

PloS one
Multi-objective production scheduling faces the problems of inter-objective conflicts, many uncertainty factors and the difficulty of traditional optimization algorithms to deal with complexity and ambiguity, and there is an urgent need to introduce ...

Predicting the sonication energy for focused ultrasound surgery treatment of breast fibroadenomas using machine learning algorithms.

International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group
PURPOSE: To establish a predictive model for the sonication energy required for focused ultrasound surgery (FUS) of breast fibroadenomas.

A novel machine-learning algorithm to screen for trisomy 21 in first-trimester singleton pregnancies.

Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology
BACKGROUND: Antenatal screening for Trisomy 21 (T21) in the UK is performed primarily in the first trimester. Nuchal Translucency (NT), gestational age, Free β-HCG and PAPP-A are used in combination, creating the 'combined' test. Multivariate Gaussia...

oDigital pathology biomarkers for guiding radiotherapy-based treatment concepts in prostate cancer - a systematic review and expert consensus.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Current risk-stratification systems for prostate cancer (PCa) do not sufficiently reflect the disease heterogeneity, and digital pathology (DP) combined with artificial intelligence (AI) tools (DP-AI) may offer a solution to this challenge. The aim o...